Why Transformers Need Positional Encoding | Sin & Cos Explained Visually
🧭 Why can't a Transformer tell "Dog bites Man" from "Man bites Dog"?
Because without positional encoding, it literally cannot. This video breaks down the elegant sine & cosine solution that gives every word its own mathematical GPS tag.
✅ Why vanilla Transformers are completely order-blind (the permutation problem)
✅ How sin & cos waves create unique, bounded, smooth position fingerprints for every token
✅ Step-by-step: how positional encoding is added to word embeddings before Multi-Head Attention
Chapters:
0:00 The Problem — Why Transformers Are Order-Blind
4:27 Positional Encoding Approaches (Overview)
9:29 Sinusoidal Positional Encoding (Deep Dive)
17:03 Why 10,000 Dimensions? (The Design Choice)
21:35 Absolute vs Relative Position Encoding
24:40 Final Takeaway & Outro
🔗 Part of the Transformer series — watch Self-Attention first: • Self-Attention Explained: How Transformers...
🔗 Multi-Head Attention explained: • Multi-Head Attention Explained Visually | ...
🔔 Subscribe to Visual AI for visual, beginner-friendly deep dives into Transformers, LLMs, and modern AI — new video every week.
#PositionalEncoding #Transformer #AttentionIsAllYouNeed #SinCosEncoding #LLM #DeepLearning #MachineLearning #NLP #AIExplained #LearnAI #TransformerArchitecture #NeuralNetworks #GPT #BERT #AIEducation